# GAC-KAN: An Ultra-Lightweight GNSS Interference Classifier for GenAI-Powered Consumer Edge Devices

arXiv: https://arxiv.org/abs/2602.11186 · HTML: https://mlsanity.com/paper/2602.11186/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 61 references that are in the map.
- First posted on arXiv 30 Jan 2026.

## Builds on (most cited first, 17 of 17)

- [Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet](https://mlsanity.com/paper/2101.11986/) (2021)
- [KAN: Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2404.19756/) (2024)
- [GhostNet: More Features from Cheap Operations](https://mlsanity.com/paper/1911.11907/) (2019)
- [Selective Kernel Networks](https://mlsanity.com/paper/1903.06586/) (2019)
- [ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks](https://mlsanity.com/paper/1908.03930/) (2019)
- [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019)
- [Large Selective Kernel Network for Remote Sensing Object Detection](https://mlsanity.com/paper/2303.09030/) (2023)
- [6G Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2108.04973/) (2021)
- [Optimizing Space-Air-Ground Integrated Networks by Artificial Intelligence](https://mlsanity.com/paper/1808.01053/) (2018)
- [GNSS Signal Authentication via Power and Distortion Monitoring](https://mlsanity.com/paper/1702.06554/) (2017)
- [Robust Beamforming Design for Near-Field DMA-NOMA mmWave Communications With Imperfect Position Information](https://mlsanity.com/paper/2409.15894/) (2024)
- [Movable Antenna-Aided Cooperative ISAC Network with Time Synchronization error and Imperfect CSI](https://mlsanity.com/paper/2501.15410/) (2025)
- [Flexible WMMSE Beamforming for MU-MIMO Movable Antenna Communications](https://mlsanity.com/paper/2503.17718/) (2025)
- [Distortion-Aware Hybrid Beamforming for Integrated Sensing and Communication](https://mlsanity.com/paper/2507.14018/) (2025)
- [Robust Optimization for Movable Antenna-aided Cell-Free ISAC with Time Synchronization Errors](https://mlsanity.com/paper/2508.13818/) (2025)
- [Crosstalk-Resilient Beamforming for Movable Antenna Enabled Integrated Sensing and Communication](https://mlsanity.com/paper/2509.03273/) (2025)
- [ACSNet: A Deep Neural Network for Compound GNSS Jamming Signal Classification](https://mlsanity.com/paper/2504.10806/) (2025)

## Built on by (most cited first, 5 of 5)

- [Sparse Gain Radio Map Reconstruction With Geometry Priors and Uncertainty-Guided Measurement Selection](https://mlsanity.com/paper/2604.05788/) (2026)
- [Geometry-Aware Cross-Height Channel Knowledge Map Prediction for UAV-Assisted Communications With Uncertainty-Guided 3D Sensing](https://mlsanity.com/paper/2607.00887/) (2026)
- [QuaMoE-DRF: Proactive Beam and Rate Adaptation via Multimodal Dynamic Radio Map Forecasting in ISAC Networks](https://mlsanity.com/paper/2607.00974/) (2026)
- [Efficient Network Inference via Hardware-Aware Architecture Search, Model Pruning & Quantization](https://mlsanity.com/paper/2606.23210/) (2026)
- [SPOC-Net: Single-Primitive Online Composition Network for GNSS Jamming Set Recognition](https://mlsanity.com/paper/2609.34875/) (2026)

## Abstract (arXiv)

The integration of Generative AI (GenAI) into Consumer Electronics (CE)--from AI-powered assistants in wearables to generative planning in autonomous Uncrewed Aerial Vehicles (UAVs)--has revolutionized user experiences. However, these GenAI applications impose immense computational burdens on edge hardware, leaving strictly limited resources for fundamental security tasks like Global Navigation Satellite System (GNSS) signal protection. Furthermore, training robust classifiers for such devices is hindered by the scarcity of real-world interference data. To address the dual challenges of data scarcity and the extreme efficiency required by the GenAI era, this paper proposes a novel framework named GAC-KAN. First, we adopt a physics-guided simulation approach to synthesize a large-scale, high-fidelity jamming dataset, mitigating the data bottleneck. Second, to reconcile high accuracy with the stringent resource constraints of GenAI-native chips, we design a Multi-Scale Ghost-ACB-Coordinate (MS-GAC) backbone. This backbone combines Asymmetric Convolution Blocks (ACB) and Ghost modules to extract rich spectral-temporal features with minimal redundancy. Replacing the traditional Multi-Layer Perceptron (MLP) decision head, we introduce a Kolmogorov-Arnold Network (KAN), which employs learnable spline activation functions to achieve superior non-linear mapping capabilities with significantly fewer parameters. Experimental results demonstrate that GAC-KAN achieves an overall accuracy of 98.0%, outperforming state-of-the-art baselines. Significantly, the model contains only 0.13 million parameter--approximately 660 times fewer than Vision Transformer (ViT) baselines. This extreme lightweight characteristic makes GAC-KAN an ideal "always-on" security companion, ensuring GNSS reliability without contending for the computational resources required by primary GenAI tasks.